335 citations · 1.3k across the 25 of their papers we have counts for
10 papers · 1 filter
Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models
Yanchen Wang, Adam Turnbull, Tiange Xiang +6
Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades,…
Advancing Multimodal Medical Capabilities of Gemini
Lin Yang, Shawn Xu, Andrew Sellergren +44
Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…
Generative models improve fairness of medical classifiers under distribution shifts
Ira Ktena, Olivia Wiles, Isabela Albuquerque +9
A ubiquitous challenge in machine learning is the problem of domain generalisation. This can exacerbate bias against groups or labels that are underrepresented in the datasets used…
Synthetic Data from Diffusion Models Improves ImageNet Classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia +2
Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models…
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti +3
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the le…
Robust and Efficient Medical Imaging with Self-Supervision
Shekoofeh Azizi, Laura Culp, Jan Freyberg +31
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach clinical expert level performance. However, such systems tend to demonstrate sub-optima…
Deep Bregman Divergence for Contrastive Learning of Visual Representations
Mina Rezaei, Farzin Soleymani, Bernd Bischl +1
Deep Bregman divergence measures divergence of data points using neural networks which is beyond Euclidean distance and capable of capturing divergence over distributions. In this…
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi +18
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data avai…
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
Zili Yi, Qiang Tang, Shekoofeh Azizi +2
Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These meth…
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
Saeid Asgari Taghanaki, Kumar Abhishek, Shekoofeh Azizi +1
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linea…